Building Ethical AI in Organizations

Enterprise Program on Operationalizing AI Ethics, Governance, and Responsibility at Scale

MODE
Mode(Online) TYPE
Mentor Based LEVEL
Moderate

Skills you will gain:

“Building Ethical AI in Organizations” is a leadership-focused and cross-functional training program designed to operationalize ethical principles of fairness, explainability, privacy, and inclusivity into real-world AI projects and systems.

As enterprises accelerate AI adoption, ethical lapses can lead to public backlash, compliance violations, and loss of trust. This program helps stakeholders design ethics-by-design processes, build AI governance committees, conduct impact assessments, and align with regulatory frameworks such as the EU AI Act, OECD Principles, NIST AI RMF, and corporate ESG standards.

Aim:

To guide organizations in developing and embedding ethical AI frameworks, aligning innovation with accountability, transparency, and societal good while reducing regulatory, reputational, and operational risks.

Program Objectives:

Translate AI ethics principles into operational workflows
Embed responsibility in every phase of the AI lifecycle—from design to deployment
Mitigate bias, opacity, and harm in enterprise AI models
Build accountability, documentation, and stakeholder trust
Support long-term AI resilience aligned with business and social values

What you will learn?

Week 1: Foundations of Ethical AI

Module 1: Principles of Ethical AI

Chapter 1.1: What Is Ethical AI? Core Values and Global Norms
Chapter 1.2: Common Ethical Challenges in AI Systems
Chapter 1.3: Human Rights, Justice, and Autonomy in AI Contexts
Chapter 1.4: Cross-Cultural Perspectives on Fairness and Ethics

Module 2: From Ethics to Action

Chapter 2.1: Translating Ethical Principles into Organizational Policies
Chapter 2.2: Avoiding Ethics Washing and Empty Frameworks
Chapter 2.3: Ethics in Product Lifecycle: Design, Development, and Deployment
Chapter 2.4: Case Studies of Ethical Failures and Lessons Learned

Week 2: Systems, Roles, and Governance for Ethical AI

Module 3: Building Organizational Structures

Chapter 3.1: Roles and Responsibilities (Ethics Leads, Review Boards, Committees)
Chapter 3.2: Creating Cross-Functional Ethics Teams
Chapter 3.3: Integrating Ethics into Product Development and ML Ops
Chapter 3.4: Internal Training and Ethical Capacity-Building

Module 4: Tools and Frameworks for Responsible AI

Chapter 4.1: Impact Assessments (Algorithmic, Human Rights, Environmental)
Chapter 4.2: Transparency and Explainability in Practice
Chapter 4.3: Auditing, Monitoring, and Documentation Tools
Chapter 4.4: Governance Frameworks: OECD, ISO 42001, NIST AI RMF

Week 3: Accountability, Culture, and Continuous Improvement

Module 5: Accountability and Escalation Paths

Chapter 5.1: Incident Management and Red Flags in AI Systems
Chapter 5.2: Whistleblower Protections and Ethical Dissent Channels
Chapter 5.3: Reporting to Leadership, Boards, and the Public
Chapter 5.4: Aligning Ethical AI with Legal Compliance and Risk

Module 6: Culture, Strategy, and Long-Term Impact

Chapter 6.1: Shaping Organizational Culture Around Responsible Innovation
Chapter 6.2: Communicating Ethical Commitments to Stakeholders
Chapter 6.3: Metrics, KPIs, and Incentives for Ethical Performance
Chapter 6.4: Capstone: Draft an Ethical AI Strategy for Your Organization

Intended For :

CXOs, VPs, and Directors overseeing AI and data strategy
AI/ML engineers and product managers
HR, legal, and compliance officers
ESG and risk management professionals
Ethics officers and data governance leads